MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming

Yura Perov (equal contribution), Logan Graham (equal contribution), Kostis Gourgoulias, Jonathan Richens, Ciarán Lee, Adam Baker, Saurabh Johri

Keywords: Probabilistic Programming, Causal Reasoning, Counterfactual Inference, Causality, Counterfactual, Importance Sampling

TL;DR: Probabilistic Programming that Natively Supports Causal, Counterfactual Inference

Abstract: We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference. We show how this can be implemented natively in probabilistic programming. By considering the structure of the counterfactual query, one can significantly optimise the inference process. We also consider design choices to enable further optimisations. We introduce MultiVerse, a probabilistic programming prototype engine for approximate causal reasoning. We provide experimental results and compare with Pyro, an existing probabilistic programming framework with some of causal reasoning tools.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/multiverse-causal-reasoning-using-importance/code)